using of anylogic and extendsim in modelling of

AGRICULTURAL ENGINEERING
USING OF ANYLOGIC AND EXTENDSIM
IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
Latvia University of Agriculture
E-mail: ilmars.dukulis@llu.lv
Abstract
Rising oil prices, national security concerns, the desire to increase farm incomes, and a host of new and improved
technologies are propelling the European Union to set the directive for the year 2010 – each member state should
achieve at least 5.75% biofuel usage of all used transport fuel. The report on the progress made in the use of renewable
fuels shows that the average Member State of the EU has achieved only 52% of its target, and biofuels’ share in 2010
will not raise much above 4%. The prices of different biofuels are still not able to compete with oil based fuel prices.
One of the possible ways how to solve this problem is to optimize biofuel supply chains using different methods of
systems engineering. The aims of this investigation were finding out appropriate simulation tools for biofuel supply
chain modelling, development of rapeseed oil supply chains for different production types, and modelling the
developed supply chains. As the result of software survey, two packages were chosen – AnyLogic and ExtendSim Suite.
Modelling studies showed that rapeseed oil supply chain is very sensitive, because changing just single parameters
in a short scale, the actual cost price of 1 litre of oil changes considerably. Comparing the fossil diesel fuel prices with
rape oil actual cost from modelling studies, the use of oil as a fuel for farm machinery seems to be profitable. Analysis
of costs distribution shows that the greatest part is composed by rapeseed growing expenses.
Key words: biofuel, rapeseed oil, logistic, supply chain, systems engineering, modelling.
Introduction
The term ‘biofuel’ applies to any solid, liquid,
or gaseous fuel produced from organic matter.
The various biomass feedstock used for producing
biofuels can be grouped into two basic categories:
the currently available ‘first-generation’ feedstock,
which is harvested for its sugar, starch and oil
content that can be converted into liquid fuels
using conventional technology; and the ‘nextgeneration’
cellulosic biomass feedstock, which is
harvested for its total biomass and whose fibres can
be converted into liquid biofuels only by advanced
technical processes. The two primary biofuels in
use today are ethanol and biodiesel, which can
both be used in existing vehicles. Ethanol is readily
blended with gasoline, and biodiesel is blended
with petroleum-based diesel for use in conventional
diesel-fuelled vehicles. The use of pure vegetable
oil and pure biodiesel in diesel engines as well
as neat bioethanol in spark-ignition engines are
other available alternatives nowadays, but they
may require modification of engine or fuel system
components.
The European Union in its biofuels directive
has set the goal for the year 2010 – each member
state should achieve at least 5.75% biofuel usage
of all used transport fuel. Unfortunately, the report
on the progress made in the use of biofuels and
other renewable fuels in the Member States of the
European Union shows that the average Member
State achieved only 52% of its target (Biofuels
Progress Report, 2007), and biofuel share in 2010
will not raise much above 4%.
Concerning Latvia, the great number of different
regulations has been developed since 2003, for
example, the programme ‘Production and use of
biofuels in Latvia (2003-2010)’, the decree of the
Cabinet of Ministers No. 511 on the implementation
strategy of the mentioned programme, and the law
on biofuels accepted by the Saeima on April 2005.
Regardless of that, the real situation in this country
is unsatisfactory – Latvian biofuel share changed
from 0.07% in 2004 to 0.33% in 2005, but in the last
two years it has stayed almost the same.
The main reason of this seems to be economical
by nature – the prices of biofuels are not able
249

USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
to be in competition with gasoline and diesel
prices. If governments and others wish to expand
production and use of biofuels significantly at the
domestic and global levels, they will need to have
an effective wide-ranging policy strategy. The most
common policies supporting biofuels today are
blending mandates and exemptions from fuel taxes.
Other policy instruments are loan guarantees; tax
incentives for agriculture and forestry, consumers
and manufactures; preferential government
purchasing policies; and research, development,
and demonstration funding for current and nextgeneration
biofuels and technologies (Biofuels for
Transport …, 2007).
System inputs
(customer requirements)
Another possible way to enlarge the production
and use of biofuels is to optimize biofuel supply
chains using different methods of systems
engineering. Systems engineering can be defined
as a structured process for arriving at a final design
of a system. The final design is selected from a
number of alternatives that would accomplish the
same objectives and considers the total life-cycle of
the project including not only the technical merits
of potential solutions but also the costs and relative
value of alternatives. The most common structure
of the systems engineering process is shown in
Figure 1 (Bahill and Gissing, 1998).
Problem statement
Design of alternatives
System modelling
Integration with sub-systems
Launching the system
Re-evaluation
Measuring the system
System outputs
Considering that system modelling plays a very
important role in engineering process structure, it
is chosen for this investigation. Before starting the
modelling process, identification of the system is
very important.
In this investigation the system is a biofuel
logistic or supply chain. The different possible
Figure 1. The systems engineering process structure.
conversion routes for the production of different
biofuel types play a central role in the model.
These conversion routes are defined by interlinked
subprocesses, which are defined by input and
output. The simplified general outline of conversion
route definition is shown in Figure 2 (Van Thuijl et
al., 2003).
Crop
production
Crop
pre-processing
Raw material
production
Fuel grade
production
National
market
Figure 2. General outline of conversion route definition for the model.
Crop production denotes the production of biomass
and biomass residues from agriculture, forestry,
and industry. The pre-processing of the crop may consist
of several pre-treatment steps. After pre-processing
of the biomass it is converted into a raw material
(for example, bio-crude or raw ethanol), which serves
as an input for the production of fuel grade biofuels.
Both raw material production and fuel grade produc-
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USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
tion may consist of several conversion processes. Finally,
the produced biofuels are transported to the national
market, where distribution to end-consumers
takes place (in either pure form or blends with conventional
automotive fuels).
Analysing the existing solutions in modelling
of biofuel supply chains, the first ones were
found relating biomass fuel supply and collection
correspondingly in the Netherlands and the United
Kingdom (Allen et al., 1998; De Mol et al., 1997). The
most valuable information from these investigations
was used input and output data for modelling, but
researchers didn’t specify the used modelling tools.
Another problem to use these models as analogues
for biofuel logistic chain modelling in Latvia is
different modelling input parameters, for example,
raw materials, distances between objects, network
of roads, etc.
The most fundamental and recent investigations
in the modelling of biofuel supply chains were
carried out within the European Commissionsupported
project ‘Clear Views on Clean Fuels’
(Wakker et al., 2005). The objective of this research
was to develop a cost efficient biofuel strategy for
Europe in terms of biofuel production, cost and
trade, and to assess its larger impact on bioenergy
markets and trade up to 2030. Based on the biomass
availability and associated costs within EU countries,
scenarios for biofuels production and cost was
constructed using quantitative modelling tools.
Combining this with data on biofuel conversion
technologies and transport of biomass and biofuels,
the lowest cost biofuel supply chain given a certain
demand predetermined by the biofuels directive
was designed. Two complementary models were
developed in this research, the BIOTRANS model
and the ChalmersVIEWLS model.
The BIOTRANS model needs the following inputs:
costs and potentials of biomass resources (including
organic waste fractions and residues) in individual
countries, data on the possible conversion routes
containing different process steps, data on the
national and international transportation and
handling of the different products involved and data
on policy incentives. The model has been formulated
as a multi commodity network flow model
connected by country nodes. The ChalmersVIEWLS
is a regionalised energy system model. It has three
end-use sectors: electricity, transportation fuels,
and heat. Specific energy demand scenarios are
developed for each of the three sectors. In addition
to energy demand and costs, the supply potentials,
energy conversion characteristics and expansion
limitations, the initial capital stock and trade
parameters are exogenously defined.
Unfortunately, reports on these modelling
studies don’t share with used modelling equations
and used computer software also. Only results in
graphical and numerical form are available. Another
problem is that these models are too global. For
example, if the distance from Gulbene to Ventspils
in European model is inconsiderable, than in
modelling of biofuel supply chain in Latvia scale it
is very significant.
Regardless of this, analysis of these investigations
were very useful, especially studies of background
document for modelling using the BIOTRANS model
(Van Thuijl et al., 2003). As an example conversion
route for pure vegetable oil from this document is
shown in Figure 3.
Solvent
Solvent (recycled)
Oil seeds
production
Washing, flaking,
cooking, pressing
Oil
extraction
Distillation
National pure
vegetable oil market
Oil seeds
at border
Seed cakes
Pure vegetable oil
at border
Figure 3. Conversion route for pure vegetable oil.
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USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
Since the pure vegetable oil chain is the simplest
from all others and it is also the part of the biodiesel
production, rapeseed oil supply chain was chosen
for the modelling studies.
The choice of the most suitable modelling and
simulation software is very important. Of course,
for the simpliest modelling studies it’s possible to
use some of tools (for example, Solver) integrated in
spreadsheet programs. The use of powerful common
use modelling software, for example, Powersim
or Simulink is also available, but probably other
software which is specially designed for modelling
of logistics would be more suitable.
Analysing existing surveys of simulation software
tools as the most fundamental investigations made
by Lionheart Publishing Company were found out.
More than sixty different packages were compared
(Simulation Software Survey, 2007; Swain, 2005;
Samuelson and Macal, 2006). A lot of criteria were
used, for example, typical applications of the
software, primary markets for which the software is
applied, system requirements, supported operating
systems, model building characteristics, etc. Most of
these packages can be used also for supply chain
and logistic modelling, but which of them could
be the most suitable for the modelling of biofuel
logistic systems is not specified in these surveys.
Summarizing literature studies, the following
tasks were set for this investigation:
• selection of criteria for simulation software
comparison to find out the appropriate ones
for biofuel supply chain modelling;
• adaptation of previously shown conversion
route (Figure 3) to specific peculiarities of Latvia
and development of rapeseed oil supply
chains for decentralized and unitary production;
Software
AnyLogic
Arena
ExtendSim
Suite
Output Analysis
Statistics tools, various
charts, histograms, etc.
Arena Output Analyzer
for statistical analysis
Confidence intervals
are calculated at the
click of a button
Simulation software survey
Batch run or experimental
design
Simulation, optimization
(including Monte Carlo
and Sensitivity Analysis),
and custom experiments
Arena Process Analyzer
Automated execution of
different scenarios
• modelling the developed rapeseed oil supply
chains using selected simulation tools.
Materials and Methods
Basing on the typical applications of the
software as well as on computer and operating
systems requirements, eight different simulation
packages were chosen for the final evaluation –
AnyLogic, Arena, ExtendSim Suite, Flexsim Simulation
Software, ProModel Optimization Suite, ServiceModel
Optimization Suite, ShowFlow, and Simul8.
The main model building characteristics were set
as the criteria for further comparison. All necessary
information was obtained from vendor home pages
and demo models. All of these programs include
following features: graphical model construction
(icon or drag-and-drop), access to programmed
modules, run time debug, input distribution fitting,
model optimization tools, animation, import CAD
drawings, different templates, user support and
discussion area, training courses, on-site training,
and consulting. As the first discarded package
was ShowFlow, because it doesn’t support both
modelling types (discrete event and continuous
event) and real time viewing of model animation.
Seven programs satisfy other main criteria –
output analysis support, presence of tools to
run experimental design and to support model
packaging (e.g., can completed model be shared
with others who might lack the software to develop
their own model?). Despite these features are
included, they are different in particular programs.
Not all programs are able to export animations
(e.g., MPEG version that can run independent of
simulation for presentation). Description of differing
tools is summarized in Table 1.
Tools to support
packaging
Generates Java applets
and applications
with full simulationoptimization
functionality
Seamless operation, no
tools necessary
Free downloadable
player from the web site
runs the models
Export
animation
+
+
–
Table 1
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USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
Software
Flexsim
Simulation
Software
ProModel
Optimization
Suite
ServiceModel
Optimization
Suite
SIMUL8
Professional
Output Analysis
Flexsim Charts.
Outputs to Excel and
Access
Output analysis
reports and charts.
Outputs to Excel and
Access
Output analysis
reports and charts.
Outputs to Excel and
Access
Included in main
product
Batch run or experimental
design
Flexsim Experimenter
Unlimited scenarios
can be predefined
to experiment on
parameters
Unlimited scenarios
can be predefined
to experiment on
parameters
Included in main product
Tools to support
packaging
Export
animation
– +
Models packaged within
software; view using
free ProModel Player
Models packaged within
software; view using
free ProModel Player
Included in main
product
–
–
+
To make the final decision, the pricing information
and presence of academic/research versions were
taken into account too because financial resources
to purchase software were limited. As the result,
two different packages were chosen – AnyLogic and
ExtendSim Suite.
Adapting conversion route for pure vegetable
oil (Figure 3) to specific peculiarities of Latvia,
rapeseed oil supply chain was developed including
decentralized and unitary production (Figure 4).
Rapeseed
growing
Decentralized production
Rapeseed drying
and storing
Rapeseed
pressing and
purification
Rapeseed oil as a fuel
for farm’s techniques
Oil cakes for farm’s
animals
Oil cakes and rapeseed
oil for other users
Unitary production
Immediate
rapeseed supplier
(drying, cleaning,
storing may take
place)
Oil manufacturer
(drying, cleaning,
storing, pressing,
filtration, distillation
may take place)
Rapeseed oil as a fuel for
manufacturer’s techniques
Rapeseed oil for manufacturer’s
biodiesel production plant
Rapeseed from
other farmers
Rapeseed from
other countries
Rapeseed from oil
manufacturer’s
rapeseed field
Realization of oil cakes
Realization of rapeseed oil as fuel,
food or for biodiesel production
Figure 4. Rapeseed oil supply chain for decentralized and unitary production.
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USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
Transportation of intermediate products may Results and Discussion
take place between the most of two subprocesses.
The main questions to be answered from these
It can be realized by farmer, intermediate supplier or
modelling studies were:
oil manufacturer transport. Biofuel or fossil fuel can
• What is the actual cost of rapeseed oil for decentralized
and unitary production?
be used in transportation and rapeseed growing.
A lot of different input parameters for modelling
• Is the price of rapeseed oil able to compete
were used, for example, seed, fertilizer, herbicide
with fossil diesel fuel prices?
and fungicide prices and norms; ploughing,
• What is the distribution of costs in rapeseed
cultivation, sowing, fertilizing, dusting, harvesting,
oil supply chain using different production
transportation, cleaning and drying expenses;
technologies grouping expenses by categories
– growing, transportation, cleaning/dry-
rapeseed yield capacities; state-aided payments;
oil extraction expenses; possible oil cake sale
ing, and pressing/purification?
price, average rapeseed transportation distances,
Performing system simulation different scenarios
etc. Besides, some of the parameters that initially
of supply chain were executed. For example,
seem to be constant, for example, seed price and
expenses for ploughing, cultivation, sowing,
sowing norm, are changing in practice. Thus, winter
fertilizing, dusting, harvesting and transportation
rapeseed price is 7.00 LVL kg -1 and 3 kg of seed is
at farm (decentralized production) in the model
needed per 1 ha. For summer rape these numbers
can be assumed as external services or they can be
are correspondingly 5.00 LVL kg -1 and 4 kg ha -1 , but
performed by farmer’s machinery. The same situation
if only biological technologies are used on the farm
is with cleaning, drying and pressing. Oil cakes can
(‘biological rape’), 8 kg ha -1 of specially treated seed
be fed to farm animals or sold to other farmers. As
is needed.
an example, the results of simulation studies for
Models were developed using both of previously
decentralized production are summarized in Table 2.
chosen packages – AnyLogic and ExtendSim Suite.
For technical services, neighbour machinery is used,
seed pre-processing and oil extraction are done by
the farmer himself, but rape cakes are sold.
Table 2
An example of the simulation results for decentralized production
Expense item Summer rape Winter rape
Summer rape
(‘biological’)
Rapeseed growing, LVL ha -1 -447.85 -511.52 -497.74
Transportation, LVL ha -1 -8.80 -8.80 -8.80
Cleaning/drying, LVL ha -1 -14.50 -21.75 -14.50
Total state-aided repayment,
LVL ha -1 86.01 86.01 161.61
Total expenses, LVL ha -1 -385.14 -456.06 -359.43
Average rapeseed yield (8%
moisture), t ha -1 2.34 3.27 1.22
Total expenses, LVL t -1 -164.80 -139.39 -295.77
Pressing, LVL ha -1 -101.64 -142.29 -51.11
Rape cake realization, LVL
ha -1 334.96 468.95 174.18
Rape oil actual cost, LVL ha -1 -151.82 -129.40 -236.36
Rape oil quantity, l ha -1 851.35 1191.89 442.70
Rape oil actual cost, LVL l -1 -0.18 -0.11 -0.53
If only a half of oil cakes is sold, but other is used
for farm’s animals, rape oil actual cost for winter
rape, summer rape and ‘biological’ summer rape
changes correspondingly to 0.38, 0.30 and 0.73 LVL l -
1
. But, in this case, economy in forage purchase for
animals has to be taken into account. If the farmer
uses his own machinery for technical services in this
scenario, values mentioned before decrease to 0.30,
254

USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
0.24, and 0.56, respectively. Rapeseed oil supply
chain model is very sensitive, because changing
just single parameters (for example, yield capacity,
rapeseed transportation distance, or rape cake sale
price) in very short scale, the actual cost price of 1
litre of rapeseed oil changes very considerably.
How do these values look in comparison with
fossil diesel fuel prices? Let’s assume that the diesel
fuel price in the fuel tank is 0.87 LVL l -1 . The farmer
can receive the excise tax compensation (0.19 LVL l -
1
) and value added tax repayment (18%). In this case,
the final diesel fuel price for the farmer is 0.52 LVL l -
1
. If we compare this value with rape oil actual cost
for winter and summer rape, the use of oil as a fuel
for farm machinery is profitable. If only biological
technologies are used on the farm, economically
the use of oil as a fuel seems not so beneficial but,
as the quality of final products is very high, they can
be used in food (oil) and for animals (cakes), which
13%
13%
2%
2%
1%
1%
13%
3%
is also very important for the farmer.
Concerning unitary production, rape oil actual
cost was approximately the same when rape was
grown on oil manufacturer field. On the one hand,
the actual cost of growing, transportation, cleaning,
drying and pressing was less than at the farm, but,
on the other hand, maintenance expenses of plant
infrastructure raise the prime cost. If the rapeseed is
bought from the immediate supplier, rape oil actual
cost is approximately 20% lower. Unfortunately, if
the farmer desires to buy oil for use as a fuel from
this manufacturer, purchase costs are much higher
(value added tax, profit percentage, etc.).
The distribution of costs in rapeseed oil supply
chain grouping expenses by categories – growing,
transportation, cleaning/drying, and pressing/
purification – are shown in Figures 5 and 6 (for
unitary production infrastructure maintenance and
logistics expenses are assumed too).
68%
68%
Rape seed growing
Transportation
Transportation
Cleaning/drying
Cleaning/drying
Pressing/purification
Pressing/purification
Infrastucture maintenance
Infrastucture Logistics maintenance
Logistics
Figure 5. Distribution of costs in rapeseed oil supply chain for unitary production.
3%
18%
3%
21%
2%
3% 9%
2%
1%
Rape seed growing
a)
77%
b)
75%
c)
86%
Transportation
Cleaning/drying
Pressing/purification
Figure 6. Distribution of costs in rapeseed oil supply chain for decentralized
production: a – for summer rape; b – for winter rape; c – for ‘biological’ summer rape.
255

USING OF ANYLOGIC AND EXTENDSIM IN MODELLING OF BIOFUEL LOGISTIC SYSTEMS
Ilmars Dukulis
Distribution of costs shows that the greatest part
is composed of rapeseed growing expenses. As it
was mentioned before, for decentralized production
this part could be reduced using own machinery
with optimal productiveness characteristics as
well as using rapeseed oil as a fuel for technical
services as much as possible. For unitary production
it’s possible to buy rapeseed from the immediate
suppliers or farmers.
Comparing models using AnyLogic and
ExtendSim Suite, both of them showed similar results,
but AnyLogic will be more useful from the viewpoint
of sharing with others who might lack the software
to develop their own model, because this software
generates Java applets and applications with full
simulation and optimization functionality.
Conclusions
1. Besides legislative initiatives, one of the possible
ways how to enlarge the production and
use of biofuels is to optimize biofuel supply
chains using different methods of systems
engineering.
2. Analysis of existing solutions in modelling of
biofuel supply chains shows that existing solutions
in this field are not usable for Latvia
directly because this country’s area, production
capacities and other parameters are very
different from other European countries.
3. Analysing available tools for the modelling
of biofuel supply chains two different software
were chosen – AnyLogic and ExtendSim.
AnyLogic will be more useful from the viewpoint
of sharing with others who might lack
the software to develop their own model,
because this software generates Java applets
and applications with full simulation and optimization
functionality.
4. Modelling studies showed that rapeseed oil
supply chain model is very sensitive, because
changing just single parameters (for example,
yield capacity, rapeseed transportation distance,
or rape cake sale price) in a short scale,
the actual cost price of 1 litre of rapeseed oil
changes considerably. That’s why the future
investigations in modelling of biofuel supply
chains in Latvia have to be done very carefully,
building models step by step and taking
into account all parameters, which are affecting
the final biofuel price most.
5. Comparing the fossil diesel fuel price (approximately
0.52 LVL l -1 after tax compensation)
with rape oil actual cost from modelling studies,
the use of oil as a fuel for farm machinery
seems to be profitable.
6. Analysis of cost distribution shows that the
greatest part (from 68% to 86% depending
on production type) is composed of rapeseed
growing expenses. This part could be
reduced using own machinery with optimal
productiveness characteristics as well as using
rapeseed oil as a fuel for technical services
as much as possible.
Acknowledgements
The author gratefully acknowledges the
funding from European Union and European
Structural Fund (grant No. 2005/0124/VPD1/ESF/
PIAA/04/APK/3.2.3.2/0066/0067 ‘Modernization
of engineering study content at Latvia University
of Agriculture’ and No. 2004/0004/VPD1/ESF/
PIAA/04/NP/3.2.3.1./0001/0005/0067 ‘Support for
doctoral studies and postdoctoral investigations in
engineering, agricultural and forest sciences’).
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